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Published online 25 August 2005
Published in Soil Sci Soc Am J 69:1609-1616 (2005)
DOI: 10.2136/sssaj2004.0312
© 2005 Soil Science Society of America
677 S. Segoe Rd., Madison, WI 53711 USA
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Soil & Water Management & Conservation

Computed-Tomographic Measurement of Soil Macroporosity Parameters as Affected by Stiff-Stemmed Grass Hedges

Achmad Rachmana, S. H. Andersonb,* and C. J. Gantzerb

a Indonesia Center for Soil and Agroclimate Research and Development, Jl. Ir. H. Juanda 98 Bogor, Indonesia 16123
b 302 Anheuser-Busch Natural Resources Building, Dep. of Soil, Environmental and Atmospheric Sciences, Univ. of Missouri, Columbia, MO 65211

* Corresponding author (AndersonS{at}missouri.edu)


   ABSTRACT
TOP
 NOTES
 ABSTRACT
INTRODUCTION
 MATERIALS AND METHODS
 RESULTS AND DISCUSSION
 CONCLUSIONS
 REFERENCES
 
Planting stiff-stemmed grass hedges in a watershed may reduce water runoff and soil erosion, in part by altering soil macroporosity. The objective of this study was to characterize macroporosity of soils under a perennial grass hedge system for 12 yr using x-ray computed tomography (CT) and to compare CT-macroporosity results with macroporosity estimated from water retention data. Three positions were sampled: grass hedge position, deposition zone position 0.5 m upslope from grass hedges, and row crop position 7 m upslope from the hedges. Intact core samples (76 mm x 76 mm) were collected from two depths, 0 to 100 and 100 to 200 mm, with five replicates per position per depth. Number of pores (macro- and meso-), averaged across depths, in the grass hedge were nearly 2.5 times greater than those in the row crop and five times greater than in the deposition positions; however their circularity was 8.8% lower than in the row crop and 2.6% lower than in the deposition positions. The CT-measured macroporosity was significantly greater (P < 0.01) for the grass hedge position (0.056 m3 m–3) as compared with the row crop (0.014 m3 m–3) and deposition positions (0.006 m3 m–3). The fractal dimension (D) was significantly greater (P < 0.01) for the grass hedge position (D = 1.56) than in the row crop (D = 1.31) and the deposition (D = 1.12) positions. The values of all measured pore characteristics decreased with depth. Computed tomography-measured macroporosity data were comparable with macroporosity estimated from water retention data. These findings suggest that grass hedge systems have created more pores and a greater volume of macroporosity.

Abbreviations: CT, computed tomography • D, fractal dimension • RAV, relative attenuation value


   INTRODUCTION
TOP
 NOTES
 ABSTRACT
 INTRODUCTION
MATERIALS AND METHODS
 RESULTS AND DISCUSSION
 CONCLUSIONS
 REFERENCES
 
STIFF-STEMMED grass hedges are a vegetative conservation technique that has been introduced to reduce water runoff and soil erosion from land under row crop management. Hedges are planted in a series of parallel narrow strips to decrease runoff velocity, increase infiltration rate, and trap sediments (Dabney et al., 1993). One of the possible mechanisms for increasing water infiltration in grass hedges may be the increased rooting and subsequent increase in soil macropores from these perennial grasses (Rachman et al., 2004).

Macropores are important pathways for rapidly infiltrating water (Edwards et al., 1988a; Heard et al., 1988; Jarvis, 1998; Allaire-Leung et al., 2000). Large soil pores allow roots, air, and water to penetrate into the soil. Water movement in soil under surface-ponded conditions is generally dominated by flow through the macropore system. Water movement in soil with reduced macroporosity occurs through smaller pores or voids between grains or aggregates (Warner et al., 1989). For macropore and matrix flow, the shape, size, orientation, and distribution of soil pores influence the rate of water flow and retention in the soil (Rasiah and Aylmore, 1998a). Increased infiltration within a watershed was found to reduce runoff and was associated with a greater number of macropores (Edwards et al., 1988b).

Since grass hedges are perennial, soil under the grass hedges may be expected to develop relatively large and vertically continuous macropores as a result of the active growth of grass roots and associated fauna. A study was conducted to evaluate the effects of 10 yr of perennial grass hedge systems on soil physical properties (Rachman et al., 2004). They found that grass hedges decreased bulk density, increased porosity and increased saturated hydraulic conductivity.

Research is needed to better quantify soil macropores developed under grass hedge management systems. X-ray computed tomography imaging techniques might provide better quantification of these features compared with other methods. A few studies have utilized CT techniques for evaluation of management treatments influencing macropore development (Grevers and de Jong, 1994; Francis et al., 2001) and relating CT-measured soil parameters to hydraulic properties (Rasiah and Aylmore, 1998b). Rogasik et al. (1999) showed the use of dual energy x-ray CT techniques for quantifying soil phases for use in evaluating soil structure. The purpose of the proposed study was to evaluate soil macropore parameters influenced by a perennial grass hedge system in place for 12 yr. It is hypothesized that grass hedges have significantly increased soil macroporosity compared with traditional row crop management.

The objective of this study was to evaluate differences in CT-measured macroporosity parameters as affected by grass hedges relative to row crop management, and to compare CT-measured macroporosity with water retention-determined macroporosity. Computed tomography-measured macroporosity parameters include number of pores, soil macroporosity (>1000 µm diam.), soil mesoporosity (200–1000 µm diam.), and circularity as a function of depth as well as macropore fractal dimension.


   MATERIALS AND METHODS
TOP
 NOTES
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
RESULTS AND DISCUSSION
 CONCLUSIONS
 REFERENCES
 
Soil Sampling
The study was conducted at Watershed 11 of the USDA-ARS National Soil Tilth Laboratory Deep Loess Research Station near Treynor, IA. The soil was an eroded Monona silt loam (fine-silty, mixed, superactive, mesic Typic Hapludolls). These soils are characteristically very deep, well drained, and formed in loess on uplands and high stream terraces. The site had been under conventional tillage continuous corn (Zea mays L) from 1975 to 1996, no-till soybean (Glycine max L) from 1997 to 1999, and no-till corn–soybean rotation until present. The site was managed with stiff-stemmed grass hedges since 1991 to control runoff and erosion from the watershed (Kramer et al., 1999). More detailed information on soil properties at the sampling site are presented by Rachman et al. (2004).

Samples were collected on 25 Mar. 2003 in the southwestern portion of the watershed. Three sampling positions within the grass hedge system were selected representing grass hedge, deposition zone, and row crop positions (Fig. 1) . Intact core samples (76 mm diam. x 76 mm long) were removed from the three positions at two depths (0- to 100- and 100- to 200-mm depths from the soil surface) with five replicates per depth (Grossman and Reinsch, 2002). Shears were used to cut grass to the soil surface. A total of 30 intact soil samples were collected. Cores from the row crop position were taken from a non-trafficked interrow 7 m upslope from the grass hedge. Cores from the deposition zone were taken approximately 0.5 m upslope of the grass hedge. Cores from the grass hedge position were taken under switchgrass (Panicum virgatum L.). The cores were collected with Plexiglas cylinders with 4-mm wall thickness. A plastic cap was placed on each end of the soil core and secured with masking tape. The soil cores were then placed in sealed plastic bags. The samples were transported to the laboratory and stored in a refrigerator at 4°C.



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  		Fig. 1. Schematic sketch of grass hedge system illustrating the width of hedge (W1), width of cropped area (W2), original soil slope (So), and sampling positions (grass hedge, deposition zone 0.5 m upslope of the hedge, and row crop 7 m upslope of the hedge).

 
Laboratory Analysis and Scanning
The bottom end of the cores was covered with two layers of fine nylon mesh to contain soil within cylinders. Cores were slowly (10 mL min–1) saturated from the bottom with distilled water containing 6.24 g L–1 CaCl2 and 1.49 g L–1 MgCl2 to minimize clay dispersion using a Mariotte system. The CaCl2 and MgCl2 concentrations approximate the average exchangeable Ca:Mg ratio for the Monona silt loam (Palmer, 1979). Samples were drained and equilibrated to –3.5 kPa using a glass-bead tension table. Samples were weighed and transported to the CT scanner for measurement. Three phantoms [(i) distilled water in (ii) an aluminum tube, and (iii) a solid copper wire] were attached to each core before scans were taken. These phantoms, along with the Plexiglas container and the surrounding air, were used to assure that data were comparable between scans for different treatments.

The CT scanner used in this study was a Siemens1 Somatom Plus 4 Volume Zoom located at the University of Missouri Hospital and Clinics. The scan system parameters were set to 125 kV, 400 MA-s and 1.5-s scan time to provide detailed and low noise projections. The field of view, that is, the cross-sectional dimension (across the core diameter), was 100 mm with 512 by 512 picture elements (pixels) giving a pixel size of 0.19 by 0.19 mm. The x-ray beam width or "slice" thickness was 0.5 mm (along the core length); therefore producing a volume element (voxel) size of 0.018 mm3. Ten scan slices per core were taken. Slices were taken in pairs adjacent to each other. Five pairs of scans were taken in each core with a distance between each pair of 10 mm. The first scan slice was taken at a 15-mm distance from the top of the soil core. Since the data for an adjacent pair of scans were nearly identical, only the upper scans were analyzed. After scanning, the wet cores were oven-dried to determine water content and bulk density. Water retention data were obtained from the Rachman et al. (2004) study.

Image Analysis
The scanned images were analyzed for macropore (>1000-µm diam.) and mesopore (200- to 1000-µm diam.) characteristics using the ImageJ version 1.27 computer software package (Rasband, 2002). The macropore and mesopore characteristics analyzed included the number of pores, pore area, perimeter of pores, macropore fractal dimension (fractal D), and pore circularity. An equivalent radius of each pore was calculated assuming the pore areas were represented by circular pores. Macroporosity and mesoporosity at each depth were calculated from the total area of all macropores and mesopores isolated in the image at a given depth divided by the cross-sectional area of the selected region on the soil core image.

The Region of Interest (ROI) tool was used to select a circular region of 73-mm diam. in each image. This region was selected to exclude voids near the core walls and minimize the effects of beam hardening due to the Plexiglas cylinder. The Clear Outside tool was used to clear areas outside the selected region. The Threshold tool was used to partition pores from solids after converting the image into an 8-bit grayscale image. The threshold values selected to analyze all images were from zero to 20 grayscale (range is 0 to 255). These grayscale values are the relative attenuation values (RAV) that are related to relative porosity (Gantzer and Anderson, 2002). The Analyze Particles tool was used to measure statistics of individual pores. The statistics of the pores included size and frequency of pores. Once pores were identified, they were analyzed to determine the fractal D for the image of soil pores by obtaining the negative slope (fractal D) of the log (number of boxes) versus log (box size). The threshold values for estimating the fractal dimension of the macropores were from zero to 100 RAV or grayscale. The higher RAV (100) for fractal D analyses as compared with pore characteristic analyses (20) was to increase data density for the deposition position. Fractal dimensions were analyzed within a 48 mm by 48 mm square image.

Statistical Analysis
A test of homogeneity of variance (F-test between the largest and smallest position variances) among positions was conducted to determine whether a further analysis of variance could be conducted due to the systematic arrangement of the positions. If there were no significant differences among position variances, an analysis of variance was done assuming a completely randomized design with soil depth as a split-plot.

The GLM procedure in the SAS program (SAS Institute, 1999) was used with significance set at P = 0.05. Significant differences between position means were assessed using the LSD (Least Significant Difference) procedure at a 95% probability level (Duncan's LSD). An estimate for the LSD between positions at the same depth or different depths was obtained using the MIXED procedure in SAS. A t test comparison for differences between CT-estimated macroporosity and water retention-estimated macroporosity was conducted since these analyses were done on separate core samples. Simple regression analyses were performed between fractal D and CT-measured macroporosity.


   RESULTS AND DISCUSSION
TOP
 NOTES
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS AND DISCUSSION
CONCLUSIONS
 REFERENCES
 
Soil Density
Results of the influence of sampling position and soil core depth on the RAV are shown in Fig. 2A and 2B for the upper scan of each depth for a typical replicate. The RAV graphs show that the data for the three positions were negatively skewed. The kurtosis for the grass hedge position was smaller for both depths (0.26; 2.64) compared with the row crop (3.79; 15.56) and deposition (16.34; 13.18) positions indicating the flatter frequency distribution for this position. Since the RAV have been found to be linearly correlated with soil density and water content (Petrovic et al., 1982; Anderson et al., 1988), these graphs provide information on the relative wet density of the soil for the three positions at the –3.5 kPa soil water pressure. The high frequency of RAV values between 125 and 170 in the deposition position indicates that soil for this position was probably denser than the other two positions for both depths. The grass hedge position has the lowest soil density and the row crop position was intermediate. Figure 2 also shows that soil density for the second depth was greater than for the first depth. These trends correspond to the measured wet and dry soil bulk densities as presented in Table 1. The grass hedge position has a significantly (P < 0.05) lower bulk density as compared with the row crop and deposition positions. The second depth for all treatments has a significantly higher (P < 0.05) bulk density compared with the first depth (Table 1).



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  		Fig. 2. Frequency of the relative attenuation values for soil under each position for the (A) 0- to 100-mm and (B) 100- to 200-mm soil depths. The top scan of a typical replicate is shown.

 

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  		Table 1. Average wet (–3.5 kPa) and dry bulk density of soil for the grass hedge, row crop, and deposition positions measured on a bulk soil basis at two soil depths (n = 5). Values in parentheses are standard errors.

 
Number of Pores
Number of pores referred to in this section refers to CT-measured pores, which indicates the lower limit of resolution on detecting pores and is directly related to the scanner resolution. The number of all pores as determined using CT analysis was significantly greater in the grass hedge position as compared with the other two positions (Table 2, Fig. 3) . The CT-measured number of pores in the grass hedge position decreased from 253 ± 88 at the layer near the surface to 83 ± 45 at the 170-mm depth. The number of pores in the deposition position was nearly constant for the soil depths studied. In the row crop position, there was a relatively greater number of pores as compared with the deposition position for the first five depth increments; however, there were no significant differences for the second five depths for these two positions. This suggests that the grass hedge system has created 2.3 times greater number of pores as compared with the row crop position and five times greater number of pores than the deposition position. Similar results were found by Chan and Mead (1989) who reported that permanent pasture created two times greater number of pores (>500-µm diam.) than soil under no-till management. Warner et al. (1989) used CT methods to characterize macropores created by earthworms, root channels, and soil cracks. They found that macropores >1 mm in diameter were easily distinguished by the method. The lower number of pores in the depositional position for this study is possibly due to accumulation of sediment (silt particles), sealing of pores, and/or lack of development of soil macroaggregate structure.


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  		Table 2. CT-measured macropores, mesopores, number of pores, box-counting fractal dimension, and circularity as affected by position and depth 10 yr after establishment of a stiff-stemmed grass hedge system (n = 25).

 


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  		Fig. 3. Mean number of pores by soil depth for the three positions measured with CT images. Bar indicates LSD(0.05) value.

 
Macroporosity and Mesoporosity
The CT-measured macroporosity was found to be significantly higher for the grass hedge position compared with the row crop and deposition positions (Table 2, Fig. 4) . Figure 5 presents grayscale and binary images of soil samples from the grass hedge (Fig. 5A and 5B), row crop (Fig. 5C and 5D), and deposition positions (Fig. 5E and 5F). The grass hedge position had more CT-measured macroporosity and CT-measured mesoporosity than the other two positions; the row crop position had more pores than the deposition position. There were significant differences (P < 0.05) between the row crop and deposition positions for the CT-measured mesoporosity (Table 2). Within the first soil core depth, the grass hedge position had the greatest CT-measured macroporosity (0.084 m3 m–3) followed by the row crop (0.025 m3 m–3) and the deposition positions (0.010 m3 m–3). In a study comparing tillage systems, Gantzer and Anderson (2002) reported CT-measured macroporosity values of 11% for soil under conventional tillage and 5% for soil under no-till management (Mexico silt loam). Grevers and de Jong (1994) using CT methods found greater macroporosity for soils under subsoil tillage management (26.5%) compared with compacted soil (7.6%). Thus, CT measurement techniques can determine differences in soil macropores among different soil management systems.



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  		Fig. 4. Computed tomography-measured macroporosity (>1000 µm diam) as a function of depth for the three positions. Bar indicates LSD(0.05) value.

 


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  		Fig. 5. Computed tomography images for the three positions at 20-mm soil depth: (A) grass hedge grayscale, (B) grass hedge binary, (C) row crop grayscale, (D) row crop binary, (E) deposition grayscale, and (F) deposition binary.

 
Computed tomography-measured macroporosity decreased with depth, with a more pronounced decrease in the grass hedge position as compared with the other two positions (Fig. 4). Macroporosity in the grass hedge position was significantly higher as compared with the other two positions for most soil depths studied. Macroporosity in the row crop position was significantly different than values in the deposition position (Table 2). For the grass hedge position, the macroporosity was higher near the soil surface (0.113 ± 0.034 m3 m–3) then decreased with depth to the lowest value (0.019 ± 0.015 m3 m–3) at the 148-mm soil depth. These higher macroporosity values in the grass hedge position reflected the high potential for conducting water in soil under grass hedges (Rachman et al., 2004). Lower macroporosity in the row crop and deposition positions for the second group of five depths may reduce water transport through soils under these treatments.

The grass hedge position had significantly higher CT-measured mesoporosity than the other two positions (Fig. 6) . Mesoporosity tended to decrease with depth for the grass hedge position. There were significant differences found for mesoporosity between the row crop and the deposition positions (Table 2).



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  		Fig. 6. Computed tomography-measured mesoporosity (200–1000 µm diam) as a function of depth for the three positions. Bar indicates LSD(0.05) value.

 
Water Retention-Estimated Macroporosity
Differences between CT-measured macroporosity and water retention-estimated macroporosity (measured on different soil cores from Rachman et al., 2004) were not statistically significant (P > 0.05) except for the row crop treatment in the second core depth (Table 3). A significant difference for the row crop treatment for the second depth may have been due to spatial variability or differences in the ability of the CT method to detect macropores for this treatment at that depth. The tendency was for the water retention-estimated macroporosity to be slightly higher than the CT-measured macroporosity except for the grass hedge position in the first core depth. These results indicate that using the CT method with image analysis can obtain similar macroporosity results to water-retention data from bulk core samples.


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  		Table 3. Computed tomography (CT) measured macroporosity and macroporosity estimated from water retention data{dagger} for grass hedge, row crop, and deposition positions at two soil core depths.

 
Gantzer and Anderson (2002) chose a threshold value of 60 to analyze their images from a different scanner, which is slightly higher than the value used for this study. The threshold value selected for this study was based on a calibration made between measured equivalent pore-diameters of large pores (>1-mm diam.) identified in the image with isolated pores using the software. The calibrations were made for macropores from the grass hedge position; the calibrated threshold value was then used to analyze all of the images. Using the calibrated threshold values resulted in nonsignificant differences between the CT-measured and water retention-measured macroporosity for the grass hedge position. Statistically significant differences occurred for the row crop position for the second depth. Using larger pores may not be representative for a mean threshold that holds for all pore wall geometries for the soil. Also, increasing the threshold value may increase CT-measured macroporosity for the other positions. Future work is necessary to more thoroughly evaluate and compare these methods.

Computed tomography-measured macroporosity values were compared with measured saturated hydraulic conductivity, Ksat (Rachman et al., 2004). Results indicate that CT-measured macroporosity was positively correlated with Ksat (r = 0.95, n = 6) suggesting the possibility that this method may provide a useful index related to soil hydraulic properties. Water-retention estimated macroporosity was also positively correlated with measured Ksat (r = 0.93, n = 6). Due to the low number of comparisons available from this study, further work is needed to provide a more complete evaluation of these relationships for a wider range of soils.

Macropore Fractal Dimension
Estimates of the box-counting fractal D for macroporosity in the three positions are shown in Table 2 and Fig. 7 . The fractal dimension is related to the number of macropores and their size distribution since it measures the space-filling nature of the macropores. Consistent with macroporosity data, the fractal D in the grass hedge position was found to have a significantly greater value (P < 0.05) compared with values for the other two positions for the two soil core depths studied (Table 2). This implies that the macropores in the grass hedge position were more space filling. Significant differences in fractal D were found between the row crop and deposition positions (Table 2). The fractal D for the first soil core depth was 1.70 for the grass hedge position, 1.49 for the row crop position, and 1.16 for the deposition position. The fractal D reported by Gantzer and Anderson (2002) for a no-till treatment was 1.26, which is smaller than values found in the row crop position in this study but is greater than values for the deposition position.



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  		Fig. 7. Box-counting fractal dimension (D) of CT-measured porosity as a function of soil depth for the three positions. Bar indicates LSD(0.05) value.

 
Fractal D decreased with depth (Fig. 7), as did CT-measured macroporosity (Fig. 4). These two parameters were found to be positively correlated with a coefficient of determination ranging from 0.74 in the deposition position, 0.90 in the row crop position, and 0.97 in the grass hedge position (Fig. 8) . These results are in agreement with other researchers regarding relationships between fractal dimension and porosity (Peyton et al., 1994; Zeng et al., 1996; Rasiah and Aylmore, 1998a).



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  		Fig. 8. Fractal dimension of CT-measured macroporosity for the (A) grass hedge, (B) row crop, and (C) deposition positions (n = 5).

 
Pore Circularity
Estimates of pore circularity in the three positions are shown in Table 2 and Fig. 9 . Pore circularity is a measure of the shape of the macropores; higher values of circularity indicate that pores are more circular. Circularity values (C) were calculated using the following equation:

[1]
where Ap is the pore area and Pp is the pore perimeter (Podczeck, 1997). If the pore is a perfect circle, the circularity is 1.0. If two pore areas are similar, then the pore with a more irregular surface will have a higher measured perimeter and a lower circularity value.



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  		Fig. 9. Pore circularity of CT-measured porosity as a function of soil depth for the three positions. Bar indicates LSD(0.05) value.

 
Circularity values in the row crop position were significantly greater (7%; P < 0.01) than values in the deposition position and nearly 10% greater than in the grass hedge position (Table 2). These results indicate that macropores in the row crop position were more circular than macropores in the grass hedge and deposition positions. This implies that the pore perimeters were greater and more irregular for the deposition and grass hedge positions relative to the row crop position. We speculate that these differences in pore perimeters may result from better soil aggregation, root activity, and soil macrofauna as affected by the grass hedge system. Macropores at deeper depths in the row crop and grass hedge positions tend to be more circular than macropores found at shallower depths (Fig. 9).


   CONCLUSIONS
TOP
 NOTES
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS AND DISCUSSION
 CONCLUSIONS
REFERENCES
 
This study hypothesized that perennial grass hedges would significantly increase soil macroporosity as compared with traditional row crop management. It was also hypothesized that CT scanning techniques along with image analysis may provide a useful procedure to quantify macroporosity parameters as affected by management. Computed tomography-measured number of pores, macroporosity, mesoporosity and fractal D of porosity were found to be significantly increased under perennial grass hedges as compared with row crop management. Computed tomography-measured pore circularity was higher under row crop management compared with soil under grass hedges and within the deposition zone. Results from the study indicate that using fractal dimension is a useful parameter in describing soil porosity values.

This study found that CT-measured macroporosity data were comparable with macroporosity estimated from water retention data. The CT method appears promising but further work is needed to more thoroughly develop this method for determining macroporosity.

These findings show that perennial grass hedges created more pores and greater area of macroporosity, which will have a significant impact on infiltration and runoff for soils under this management system. This study also shows the usefulness of CT-scanning techniques combined with image analysis for quantifying macropore parameters. The CT-scanning technique is a more direct measure of soil pores and can evaluate the spatial variations in these parameters both across and within core samples, which may be impossible with more traditional techniques such as water retention (Anderson et al., 2003). This is a major advantage of CT-scanning methods. These nondestructive techniques will prove useful for similar studies in the future and will further expand our knowledge of soil pore systems. This technique can be applied to soil pore characterization studies for different management systems such as tillage, riparian zones, and soil quality restoration as well as geotechnical investigations.


   ACKNOWLEDGMENTS
 
The authors are grateful to the Agency for Agricultural Research and Development (AARD), Ministry of Agriculture, Indonesia, in providing the first author with financial support to study in the USA. This research was supported in part by the Missouri Agricultural Experiment Station project number MO-NRSL0117. The authors are grateful to Mr. Larry Kramer for identifying sampling positions and Ms. Faith Oxford for assistance with scanning soil cores.


   NOTES
TOP
 NOTES
ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS AND DISCUSSION
 CONCLUSIONS
 REFERENCES
 
Contribution from the Missouri Agricultural Experiment Station.

1 Does not indicate any preference over other scanners. Back

Received for publication September 21, 2004.


   REFERENCES
TOP
 NOTES
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS AND DISCUSSION
 CONCLUSIONS
 REFERENCES
 




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Right arrow Articles by Rachman, A.
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The SCI Journals Agronomy Journal Crop Science
Journal of Natural Resources
and Life Sciences Education		 Vadose Zone Journal
Journal of Plant Registrations Journal of
Environmental Quality		 The Plant Genome